Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "10 May 2023 10 58AM/All"
postexperiment_date <- "10 May 2023 03 49PM/All"

##--- last fish run in trial ---##
experiment_date <- "10 May 2023 12 19PM/Oxygen"
experiment_date2 <- "10 May 2023 12 19PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "10 May 2023 11 43AM/All"
postexperiment_date_asus <- "10 May 2023 04 49PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "10 May 2023 01 37PM/Oxygen"
experiment_date2_asus <- "10 May 2023 01 37PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791

Date_tested="2023-05-10"
Clutch = "96" 
Male = "CARL338" 
Female = "CARL345"
Population = "Arlington reef"
Tank =105 
salinity =36 
Date_analysed = Sys.Date() 

Replicates

1

Enter specimen data

Replicate = 1 
mass = 0.0005109 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "10 May 2023 11 49AM/Oxygen"
experiment_mmr_date2 <- "10 May 2023 11 49AM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001141891

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.004126875

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  4  5  6  7  9 11 13 14 15 17 18 19 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   5    1     241.2334 -0.01994887 0.997      NA 1959   2188  7160.88
## 2:  13    1     323.2519 -0.01958514 0.993      NA 5871   6104 11479.78
## 3:  15    1     370.2043 -0.02161477 0.998      NA 6859   7092 12559.70
## 4:  16    1     372.8820 -0.02092523 0.996      NA 7354   7587 13100.83
## 5:  17    1     368.6510 -0.01980262 0.998      NA 7847   8080 13639.77
## 6:  20    1     396.2138 -0.01949804 0.997      NA 9329   9562 15260.19
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  7415.57 98.258 93.235 -0.01994887 -0.002002400   -0.01794647 -0.01794647
## 2: 11734.60 98.340 93.671 -0.01958514 -0.003284298   -0.01630084 -0.01630084
## 3: 12814.64 98.680 93.101 -0.02161477 -0.003604843   -0.01800993 -0.01800993
## 4: 13355.47 98.758 93.367 -0.02092523 -0.003765409   -0.01715982 -0.01715982
## 5: 13894.73 98.614 93.440 -0.01980262 -0.003925418   -0.01587720 -0.01587720
## 6: 15514.98 98.658 93.807 -0.01949804 -0.004406344   -0.01509170 -0.01509170
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1989092
## 2:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1806699
## 3:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1996126
## 4:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1901904
## 5:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1759746
## 6:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.1672685
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -389.3310          NA  mgO2/hr/kg   -389.3310
## 2:   -353.6307          NA  mgO2/hr/kg   -353.6307
## 3:   -390.7077          NA  mgO2/hr/kg   -390.7077
## 4:   -372.2655          NA  mgO2/hr/kg   -372.2655
## 5:   -344.4403          NA  mgO2/hr/kg   -344.4403
## 6:   -327.3996          NA  mgO2/hr/kg   -327.3996
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 1 CARL338 CARL345 Arlington reef 105 0.0005109 ch4 Dell 0.0486 2023-05-10 2024-06-18 good/good 36 28.5 370.075 0.1890713 0.9964

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     266.3945 -0.05201289 0.9875144      NA 113    166 3274.35
##   2:  NA    2     266.3689 -0.05200614 0.9874963      NA 112    165 3273.26
##   3:  NA    3     266.1336 -0.05193619 0.9872657      NA 111    164 3272.03
##   4:  NA    4     266.1300 -0.05193169 0.9872743      NA 114    167 3275.43
##   5:  NA    5     265.9667 -0.05188735 0.9870704      NA 110    163 3270.94
##  ---                                                                       
## 209:  NA  209     155.3080 -0.01778804 0.9906779      NA   8     61 3155.68
## 210:  NA  210     155.2822 -0.01777977 0.9906339      NA   9     62 3156.77
## 211:  NA  211     155.2606 -0.01777321 0.9906598      NA   7     60 3154.58
## 212:  NA  212     155.1762 -0.01774675 0.9907202      NA   5     58 3152.07
## 213:  NA  213     155.0885 -0.01771912 0.9906413      NA   6     59 3153.44
##      endtime    oxy endoxy        rate
##   1: 3334.35 95.989 93.051 -0.05201289
##   2: 3333.26 96.033 93.114 -0.05200614
##   3: 3332.03 96.041 93.219 -0.05193619
##   4: 3335.43 95.904 93.037 -0.05193169
##   5: 3330.94 96.064 93.276 -0.05188735
##  ---                                  
## 209: 3215.68 99.175 98.108 -0.01778804
## 210: 3216.77 99.154 98.103 -0.01777977
## 211: 3214.58 99.190 98.111 -0.01777321
## 212: 3212.07 99.286 98.186 -0.01774675
## 213: 3213.44 99.223 98.163 -0.01771912
## 
## Regressions : 213 | Results : 213 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 213 adjusted rate(s):
## Rate          : -0.05201289
## Adjustment    : -0.001141891
## Adjusted Rate : -0.050871 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 213 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 212 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     266.3945 -0.05201289 0.9875144      NA 113    166 3274.35
##    endtime    oxy endoxy        rate   adjustment rate.adjusted rate.input
## 1: 3334.35 95.989 93.051 -0.05201289 -0.001141891     -0.050871  -0.050871
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0005109   NA 36 28.5 1.013253 -0.5638273
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1103.596          NA  mgO2/hr/kg   -1103.596
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 1 CARL338 CARL345 Arlington reef 105 0.0005109 ch4 Dell 0.0486 2023-05-10 2024-06-18 good/good 36 28.5 370.075 0.1890713 0.9964 1103.596 0.5638273 0.9875144 733.5212 0.374756

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 123 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Replicate = 2 
mass = 0.0006801 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "10 May 2023 11 59AM/Oxygen"
experiment_mmr_date2 <- "10 May 2023 11 59AM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001856995

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003526672

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  4  5  6  7  9 11 13 14 15 17 18 19 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   7    1     242.4835 -0.01740181 0.991      NA 2927   3159  8240.04
## 2:  10    1     263.1279 -0.01659797 0.955      NA 4396   4628  9860.94
## 3:  12    1     307.9366 -0.01914253 0.996      NA 5379   5612 10940.91
## 4:  13    1     296.3598 -0.01719334 0.995      NA 5871   6104 11479.78
## 5:  17    1     351.1987 -0.01847838 0.966      NA 7847   8080 13639.77
## 6:  18    1     326.8014 -0.01606404 0.992      NA 8341   8574 14180.49
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  8495.36 98.786 94.249 -0.01740181 -0.002517545   -0.01488426 -0.01488426
## 2: 10116.15 99.028 94.583 -0.01659797 -0.002786639   -0.01381133 -0.01381133
## 3: 11196.28 98.559 93.284 -0.01914253 -0.002965951   -0.01617658 -0.01617658
## 4: 11734.60 98.885 94.604 -0.01719334 -0.003055369   -0.01413797 -0.01413797
## 5: 13894.73 98.666 93.999 -0.01847838 -0.003413985   -0.01506439 -0.01506439
## 6: 14435.15 99.111 95.010 -0.01606404 -0.003503731   -0.01256031 -0.01256031
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1689408
## 2:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1567627
## 3:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1836090
## 4:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1604702
## 5:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1709853
## 6:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.1425633
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -248.4059          NA  mgO2/hr/kg   -248.4059
## 2:   -230.4994          NA  mgO2/hr/kg   -230.4994
## 3:   -269.9736          NA  mgO2/hr/kg   -269.9736
## 4:   -235.9509          NA  mgO2/hr/kg   -235.9509
## 5:   -251.4121          NA  mgO2/hr/kg   -251.4121
## 6:   -209.6210          NA  mgO2/hr/kg   -209.6210
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 2 CARL338 CARL345 Arlington reef 105 0.0006801 ch3 Dell 0.04977 2023-05-10 2024-06-18 good/good 36 28.5 247.2484 0.1681536 0.9806

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row,  
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.37
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     348.1781 -0.06547296 0.9918205      NA  77    131 3853.05
##   2:  NA    2     347.9904 -0.06542332 0.9916949      NA  78    132 3854.15
##   3:  NA    3     347.7327 -0.06535935 0.9916045      NA  76    130 3851.88
##   4:  NA    4     347.4082 -0.06527700 0.9914317      NA  75    129 3850.79
##   5:  NA    5     347.2166 -0.06522264 0.9911419      NA  79    133 3855.25
##  ---                                                                       
## 212:  NA  212     158.9855 -0.01719758 0.9326232      NA 158    212 3943.17
## 213:  NA  213     158.9112 -0.01718201 0.9332961      NA 160    214 3945.40
## 214:  NA  214     158.8232 -0.01716115 0.9336515      NA 161    215 3946.49
## 215:  NA  215     158.7144 -0.01713105 0.9345374      NA 159    213 3944.32
## 216:  NA  216     158.6469 -0.01711843 0.9280906      NA 162    216 3947.64
##      endtime    oxy endoxy        rate
##   1: 3913.05 95.792 92.063 -0.06547296
##   2: 3914.15 95.741 92.062 -0.06542332
##   3: 3911.88 95.848 92.162 -0.06535935
##   4: 3910.79 95.877 92.227 -0.06527700
##   5: 3915.25 95.691 92.062 -0.06522264
##  ---                                  
## 212: 4003.17 91.372 89.987 -0.01719758
## 213: 4005.40 91.260 89.920 -0.01718201
## 214: 4006.49 91.187 89.939 -0.01716115
## 215: 4004.32 91.287 89.951 -0.01713105
## 216: 4007.64 91.167 89.880 -0.01711843
## 
## Regressions : 216 | Results : 216 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 216 adjusted rate(s):
## Rate          : -0.06547296
## Adjustment    : -0.001856995
## Adjusted Rate : -0.06361596 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 34 rate(s) removed, 182 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 181 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     348.1781 -0.06547296 0.9918205      NA  77    131 3853.05
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 3913.05 95.792 92.063 -0.06547296 -0.001856995   -0.06361596 -0.06361596
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0006801   NA 36 28.5 1.013253 -0.7220601
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1061.697          NA  mgO2/hr/kg   -1061.697
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 2 CARL338 CARL345 Arlington reef 105 0.0006801 ch3 Dell 0.04977 2023-05-10 2024-06-18 good/good 36 28.5 247.2484 0.1681536 0.9806 1061.697 0.7220601 0.9918205 814.4486 0.5539065

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 124 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Replicate = 3 
mass = 0.0007235 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""

##--- time of trail ---## 
experiment_mmr_date <- "10 May 2023 12 09PM/Oxygen"
experiment_mmr_date2 <- "10 May 2023 12 09PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001190774

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003780626

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  4  5  6  7  9 11 13 14 15 17 18 19 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     370.3166 -0.02928958 0.995      NA 3906   4138  9320.80
## 2:  11    1     399.5995 -0.02898534 0.989      NA 4887   5119 10400.62
## 3:  13    1     431.5494 -0.02900477 0.994      NA 5871   6104 11479.78
## 4:  15    1     473.7636 -0.02991459 0.994      NA 6859   7092 12559.70
## 5:  17    1     521.0619 -0.03113913 0.999      NA 7847   8080 13639.77
## 6:  20    1     564.2341 -0.03056566 0.996      NA 9329   9562 15260.19
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1:  9575.44 97.033 89.935 -0.02928958 -0.002493585   -0.02679599 -0.02679599
## 2: 10655.19 98.054 90.843 -0.02898534 -0.002771648   -0.02621369 -0.02621369
## 3: 11734.60 98.454 91.162 -0.02900477 -0.003049583   -0.02595518 -0.02595518
## 4: 12814.64 98.437 90.449 -0.02991459 -0.003327696   -0.02658689 -0.02658689
## 5: 13894.73 96.265 88.332 -0.03113913 -0.003605836   -0.02753329 -0.02753329
## 6: 15514.98 97.742 90.393 -0.03056566 -0.004023100   -0.02654256 -0.02654256
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2806764
## 2:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2745771
## 3:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2718693
## 4:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2784861
## 5:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2883993
## 6:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.2780218
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -387.9425          NA  mgO2/hr/kg   -387.9425
## 2:   -379.5122          NA  mgO2/hr/kg   -379.5122
## 3:   -375.7696          NA  mgO2/hr/kg   -375.7696
## 4:   -384.9152          NA  mgO2/hr/kg   -384.9152
## 5:   -398.6168          NA  mgO2/hr/kg   -398.6168
## 6:   -384.2734          NA  mgO2/hr/kg   -384.2734
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 3 CARL338 CARL345 Arlington reef 105 0.0007235 ch2 Dell 0.04593 2023-05-10 2024-06-18 good/good 36 28.5 387.052 0.2800321 0.9946

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.07 1.67
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  7  8  9 10 11 13 14 15 16 17 18 20 21 22 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.37
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     457.2479 -0.08208680 0.9971804      NA 110    163 4453.08
##   2:  NA    2     457.1256 -0.08205819 0.9971425      NA 111    164 4454.20
##   3:  NA    3     456.9977 -0.08203233 0.9970835      NA 109    162 4451.72
##   4:  NA    4     455.8410 -0.08177049 0.9967102      NA 112    165 4455.28
##   5:  NA    5     455.6639 -0.08173613 0.9965692      NA 108    161 4450.63
##  ---                                                                       
## 205:  NA  205     303.1670 -0.04789695 0.9813608      NA 160    213 4511.18
## 206:  NA  206     302.0911 -0.04765999 0.9817500      NA 156    209 4506.48
## 207:  NA  207     301.6870 -0.04757075 0.9814117      NA 159    212 4509.84
## 208:  NA  208     301.3596 -0.04749849 0.9814552      NA 157    210 4507.57
## 209:  NA  209     301.1358 -0.04744918 0.9813931      NA 158    211 4508.75
##      endtime    oxy endoxy        rate
##   1: 4513.08 91.605 86.928 -0.08208680
##   2: 4514.20 91.537 86.861 -0.08205819
##   3: 4511.72 91.612 86.981 -0.08203233
##   4: 4515.28 91.528 86.842 -0.08177049
##   5: 4510.63 91.693 87.064 -0.08173613
##  ---                                  
## 205: 4571.18 87.150 84.109 -0.04789695
## 206: 4566.48 87.464 84.551 -0.04765999
## 207: 4569.84 87.241 84.249 -0.04757075
## 208: 4567.57 87.389 84.480 -0.04749849
## 209: 4568.75 87.292 84.365 -0.04744918
## 
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 209 adjusted rate(s):
## Rate          : -0.0820868
## Adjustment    : -0.001190774
## Adjusted Rate : -0.08089603 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 209 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 208 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0   slope_b1       rsq density row endrow    time
## 1:  NA    1     457.2479 -0.0820868 0.9971804      NA 110    163 4453.08
##    endtime    oxy endoxy       rate   adjustment rate.adjusted  rate.input
## 1: 4513.08 91.605 86.928 -0.0820868 -0.001190774   -0.08089603 -0.08089603
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0007235   NA 36 28.5 1.013253 -0.8473508
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1171.183          NA  mgO2/hr/kg   -1171.183
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 3 CARL338 CARL345 Arlington reef 105 0.0007235 ch2 Dell 0.04593 2023-05-10 2024-06-18 good/good 36 28.5 387.052 0.2800321 0.9946 1171.183 0.8473508 0.9971804 784.1308 0.5673186

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 125 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Replicate = 4 
mass = 0.0005386
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes="check max"

##--- time of trail ---## 
experiment_mmr_date <- "10 May 2023 12 19PM/Oxygen"
experiment_mmr_date2 <- "10 May 2023 12 19PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.002619577

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003681555

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  4  5  6  7  9 11 13 14 15 17 18 19 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     307.8499 -0.02125007 0.994      NA 4396   4628  9860.94
## 2:  13    1     358.4426 -0.02259662 0.995      NA 5871   6104 11479.78
## 3:  15    1     391.5116 -0.02330403 0.996      NA 6859   7092 12559.70
## 4:  16    1     378.6020 -0.02133781 0.993      NA 7354   7587 13100.83
## 5:  17    1     414.9433 -0.02321792 0.992      NA 7847   8080 13639.77
## 6:  20    1     435.5871 -0.02208878 0.987      NA 9329   9562 15260.19
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 10116.15 98.431 92.832 -0.02125007 -0.003210866   -0.01803921 -0.01803921
## 2: 11734.60 98.827 93.061 -0.02259662 -0.003381788   -0.01921483 -0.01921483
## 3: 12814.64 98.606 92.762 -0.02330403 -0.003495830   -0.01980820 -0.01980820
## 4: 13355.47 98.752 93.301 -0.02133781 -0.003552955   -0.01778486 -0.01778486
## 5: 13894.73 98.324 92.093 -0.02321792 -0.003609882   -0.01960803 -0.01960803
## 6: 15514.98 98.489 92.634 -0.02208878 -0.003780983   -0.01830780 -0.01830780
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.1912978
## 2:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.2037648
## 3:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.2100572
## 4:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.1886005
## 5:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.2079345
## 6:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.1941461
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -355.1760          NA  mgO2/hr/kg   -355.1760
## 2:   -378.3231          NA  mgO2/hr/kg   -378.3231
## 3:   -390.0060          NA  mgO2/hr/kg   -390.0060
## 4:   -350.1681          NA  mgO2/hr/kg   -350.1681
## 5:   -386.0649          NA  mgO2/hr/kg   -386.0649
## 6:   -360.4644          NA  mgO2/hr/kg   -360.4644
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 4 CARL338 CARL345 Arlington reef 105 0.0005386 ch1 Dell 0.0465 2023-05-10 2024-06-18 good/good 36 28.5 374.0069 0.2014401 0.9928

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1]  1.07 12.17
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  4  5  6  7  9 11 13 14 15 17 18 19 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.08 1.36
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     384.4542 -0.05639708 0.9575583      NA 198    251 5183.44
##   2:  NA    2     382.0471 -0.05594274 0.9538187      NA 195    248 5179.93
##   3:  NA    3     380.8639 -0.05571221 0.9564472      NA 197    250 5182.11
##   4:  NA    4     380.2642 -0.05559196 0.9554829      NA 199    252 5184.55
##   5:  NA    5     379.5769 -0.05546778 0.9544370      NA 196    249 5181.02
##  ---                                                                       
## 205:  NA  205     187.3707 -0.01844737 0.9581222      NA 165    218 5145.30
## 206:  NA  206     186.3031 -0.01823933 0.9706473      NA 164    217 5143.98
## 207:  NA  207     185.9461 -0.01816807 0.9729239      NA 162    215 5141.74
## 208:  NA  208     185.5592 -0.01809312 0.9714501      NA 161    214 5140.65
## 209:  NA  209     184.2078 -0.01783355 0.9765142      NA 163    216 5142.88
##      endtime    oxy endoxy        rate
##   1: 5243.44 91.795 89.082 -0.05639708
##   2: 5239.93 91.801 89.193 -0.05594274
##   3: 5242.11 91.792 89.093 -0.05571221
##   4: 5244.55 91.771 89.117 -0.05559196
##   5: 5241.02 91.769 89.110 -0.05546778
##  ---                                  
## 205: 5205.30 92.605 91.062 -0.01844737
## 206: 5203.98 92.613 91.177 -0.01823933
## 207: 5201.74 92.706 91.402 -0.01816807
## 208: 5200.65 92.732 91.482 -0.01809312
## 209: 5202.88 92.707 91.301 -0.01783355
## 
## Regressions : 209 | Results : 209 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 209 adjusted rate(s):
## Rate          : -0.05639708
## Adjustment    : -0.002619577
## Adjusted Rate : -0.0537775 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 28 rate(s) removed, 181 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 180 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     384.4542 -0.05639708 0.9575583      NA 198    251 5183.44
##    endtime    oxy endoxy        rate   adjustment rate.adjusted rate.input
## 1: 5243.44 91.795 89.082 -0.05639708 -0.002619577    -0.0537775 -0.0537775
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0005386   NA 36 28.5 1.013253 -0.5702866
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1058.831          NA  mgO2/hr/kg   -1058.831
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 4 CARL338 CARL345 Arlington reef 105 0.0005386 ch1 Dell 0.0465 2023-05-10 2024-06-18 good/good 36 28.5 374.0069 0.2014401 0.9928 1058.831 0.5702866 0.9575583 684.8246 0.3688465 check max

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 126 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

5

Enter specimen data

Replicate = 5 
mass = 0.0004724 
chamber = "ch4" 
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "10 May 2023 01 20PM/Oxygen"
experiment_mmr_date2_asus <- "10 May 2023 01 20PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 0.0004399073

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.001621275

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.49
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  12    1     375.6322 -0.02146258 0.995      NA 4414   4602 12906.22
## 2:  13    1     378.9661 -0.02081631 0.990      NA 4812   5000 13446.11
## 3:  17    1     432.6998 -0.02142213 0.993      NA 6403   6591 15606.06
## 4:  18    1     459.1493 -0.02233115 0.983      NA 6801   6989 16146.61
## 5:  19    1     424.5332 -0.01955872 0.983      NA 7198   7386 16685.90
## 6:  20    1     482.5596 -0.02231772 0.990      NA 7596   7784 17225.67
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 13161.20 98.648 93.343 -0.02146258 -0.001558467   -0.01990412 -0.01990412
## 2: 13701.69 98.960 93.779 -0.02081631 -0.001694567   -0.01912174 -0.01912174
## 3: 15861.63 98.259 92.805 -0.02142213 -0.002238760   -0.01918338 -0.01918338
## 4: 16402.06 98.321 93.041 -0.02233115 -0.002374935   -0.01995621 -0.01995621
## 5: 16940.93 98.265 93.575 -0.01955872 -0.002510755   -0.01704796 -0.01704796
## 6: 17481.09 98.080 92.150 -0.02231772 -0.002646798   -0.01967092 -0.01967092
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.2174747
## 2:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.2089263
## 3:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.2095998
## 4:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.2180439
## 5:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.1862680
## 6:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.2149268
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -460.3613          NA  mgO2/hr/kg   -460.3613
## 2:   -442.2658          NA  mgO2/hr/kg   -442.2658
## 3:   -443.6913          NA  mgO2/hr/kg   -443.6913
## 4:   -461.5662          NA  mgO2/hr/kg   -461.5662
## 5:   -394.3015          NA  mgO2/hr/kg   -394.3015
## 6:   -454.9677          NA  mgO2/hr/kg   -454.9677
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 5 CARL338 CARL345 Arlington reef 105 0.0004724 ch4 Asus 0.04791 2023-05-10 2024-06-18 good/good 36 28.5 452.5704 0.2137943 0.9902

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  5  6  7  8 10 11 13 14 15 17 20 21 22 24 25 26 27
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.49
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     427.9968 -0.056017613 0.9799690      NA   9     54 5890.48
##   2:  NA    2     427.9443 -0.056010458 0.9799278      NA   8     53 5889.10
##   3:  NA    3     427.6677 -0.055965430 0.9796923      NA   7     52 5887.75
##   4:  NA    4     426.9329 -0.055836467 0.9791284      NA  10     55 5891.84
##   5:  NA    5     426.5078 -0.055771217 0.9786084      NA   6     51 5886.41
##  ---                                                                        
## 172:  NA  172     141.6832 -0.007980099 0.3831860      NA  65    110 5966.46
## 173:  NA  173     138.9550 -0.007513484 0.3928244      NA  61    106 5961.00
## 174:  NA  174     137.6247 -0.007299985 0.3906666      NA  64    109 5965.08
## 175:  NA  175     136.4625 -0.007100201 0.3941171      NA  62    107 5962.37
## 176:  NA  176     136.2079 -0.007060527 0.3943587      NA  63    108 5963.71
##      endtime    oxy endoxy         rate
##   1: 5950.48 97.853 94.888 -0.056017613
##   2: 5949.10 97.913 94.944 -0.056010458
##   3: 5947.75 97.926 94.994 -0.055965430
##   4: 5951.84 97.860 94.834 -0.055836467
##   5: 5946.41 97.898 95.065 -0.055771217
##  ---                                   
## 172: 6026.46 94.359 93.020 -0.007980099
## 173: 6021.00 94.685 93.546 -0.007513484
## 174: 6025.08 94.456 93.182 -0.007299985
## 175: 6022.37 94.607 93.436 -0.007100201
## 176: 6023.71 94.506 93.314 -0.007060527
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.05601761
## Adjustment    : 0.0004399073
## Adjusted Rate : -0.05645752 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 79 rate(s) removed, 97 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 96 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1      rsq density row endrow    time
## 1:  NA    1     427.9968 -0.05601761 0.979969      NA   9     54 5890.48
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5950.48 97.853 94.888 -0.05601761 0.0004399073   -0.05645752 -0.05645752
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04791 0.0004724   NA 36 28.5 1.013253 -0.6168614
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1305.803          NA  mgO2/hr/kg   -1305.803
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 5 CARL338 CARL345 Arlington reef 105 0.0004724 ch4 Asus 0.04791 2023-05-10 2024-06-18 good/good 36 28.5 452.5704 0.2137943 0.9902 1305.803 0.6168614 0.979969 853.2326 0.4030671
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 127 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Replicate = 6 
mass = 0.0006590 
chamber = "ch3" 
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes="check max"

##--- time of trail ---## 
experiment_mmr_date_asus <- "10 May 2023 01 00PM/Oxygen"
experiment_mmr_date2_asus <- "10 May 2023 01 00PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.001752729

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.00331577

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.49
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     420.9891 -0.02741936 0.992      NA 3618   3799 11826.18
## 2:  12    1     446.7899 -0.02709840 0.982      NA 4414   4595 12906.22
## 3:  13    1     472.0694 -0.02788033 0.982      NA 4812   4992 13446.11
## 4:  14    1     439.5205 -0.02450424 0.990      NA 5210   5390 13986.36
## 5:  17    1     503.5423 -0.02607516 0.988      NA 6403   6583 15606.06
## 6:  18    1     529.9804 -0.02683246 0.993      NA 6801   6981 16146.61
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 12072.04 96.787 89.855 -0.02741936 -0.003060921   -0.02435844 -0.02435844
## 2: 13151.72 97.014 90.413 -0.02709840 -0.003267236   -0.02383116 -0.02383116
## 3: 13690.78 97.064 90.334 -0.02788033 -0.003370307   -0.02451002 -0.02451002
## 4: 14230.96 96.965 90.299 -0.02450424 -0.003473519   -0.02103073 -0.02103073
## 5: 15850.76 96.953 90.114 -0.02607516 -0.003782984   -0.02229218 -0.02229218
## 6: 16391.19 96.772 89.690 -0.02683246 -0.003886249   -0.02294622 -0.02294622
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2528110
## 2:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2473385
## 3:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2543842
## 4:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2182733
## 5:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2313657
## 6:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.2381538
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -383.6282          NA  mgO2/hr/kg   -383.6282
## 2:   -375.3239          NA  mgO2/hr/kg   -375.3239
## 3:   -386.0155          NA  mgO2/hr/kg   -386.0155
## 4:   -331.2190          NA  mgO2/hr/kg   -331.2190
## 5:   -351.0860          NA  mgO2/hr/kg   -351.0860
## 6:   -361.3866          NA  mgO2/hr/kg   -361.3866
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 6 CARL338 CARL345 Arlington reef 105 0.000659 ch3 Asus 0.04551 2023-05-10 2024-06-18 good/good 36 28.5 371.4881 0.2448106 0.9874

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  8  9 10 11 12 13 14 15 16 17 18 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.38
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     403.4957 -0.06424468 0.9963980      NA 103    148 4845.67
##   2:  NA    2     402.5075 -0.06404156 0.9965168      NA 102    147 4844.31
##   3:  NA    3     402.2856 -0.06399621 0.9962386      NA 104    149 4847.05
##   4:  NA    4     401.0073 -0.06373350 0.9966242      NA 101    146 4842.96
##   5:  NA    5     400.3579 -0.06359971 0.9967260      NA 100    145 4841.61
##  ---                                                                       
## 173:  NA  173     207.5666 -0.02374197 0.8235169      NA  57    102 4783.30
## 174:  NA  174     207.5813 -0.02373302 0.8223935      NA  52     97 4776.53
## 175:  NA  175     207.2661 -0.02367696 0.8241980      NA  56    101 4781.96
## 176:  NA  176     206.9490 -0.02360712 0.8244621      NA  54     99 4779.27
## 177:  NA  177     206.8867 -0.02359173 0.8245626      NA  53     98 4777.91
##      endtime    oxy endoxy        rate
##   1: 4905.67 92.247 88.246 -0.06424468
##   2: 4904.31 92.289 88.334 -0.06404156
##   3: 4907.05 92.183 88.339 -0.06399621
##   4: 4902.96 92.435 88.443 -0.06373350
##   5: 4901.61 92.424 88.581 -0.06359971
##  ---                                  
## 173: 4843.30 94.296 92.289 -0.02374197
## 174: 4836.53 94.752 92.524 -0.02373302
## 175: 4841.96 94.308 92.435 -0.02367696
## 176: 4839.27 94.414 92.447 -0.02360712
## 177: 4837.91 94.579 92.445 -0.02359173
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.06424468
## Adjustment    : -0.001752729
## Adjusted Rate : -0.06249195 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 40 rate(s) removed, 137 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 136 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1      rsq density row endrow    time
## 1:  NA    1     403.4957 -0.06424468 0.996398      NA 103    148 4845.67
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 4905.67 92.247 88.246 -0.06424468 -0.001752729   -0.06249195 -0.06249195
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04551 0.000659   NA 36 28.5 1.013253 -0.6485904
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -984.2039          NA  mgO2/hr/kg   -984.2039
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 6 CARL338 CARL345 Arlington reef 105 0.000659 ch3 Asus 0.04551 2023-05-10 2024-06-18 good/good 36 28.5 371.4881 0.2448106 0.9874 984.2039 0.6485904 0.996398 612.7158 0.4037797 check max
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 128 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

7

Enter specimen data

Replicate = 7 
mass = 0.0004850 
chamber = "ch2" 
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "10 May 2023 01 08PM/Oxygen"
experiment_mmr_date2_asus <- "10 May 2023 01 08PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] 5.740002e-05

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003052967

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.49
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   9    1     287.1674 -0.01665632 0.998      NA 3220   3401 11285.94
## 2:  13    1     325.5614 -0.01682268 0.984      NA 4812   4992 13446.11
## 3:  15    1     357.1047 -0.01774006 0.986      NA 5607   5788 14525.65
## 4:  17    1     340.4081 -0.01545781 0.995      NA 6403   6583 15606.06
## 5:  19    1     380.4154 -0.01684658 0.992      NA 7198   7379 16685.90
## 6:  20    1     401.3012 -0.01754700 0.990      NA 7596   7776 17225.67
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11531.66 99.101 95.243 -0.01665632 -0.002340408   -0.01431591 -0.01431591
## 2: 13690.78 99.205 95.001 -0.01682268 -0.003161492   -0.01366118 -0.01366118
## 3: 14771.39 99.100 94.803 -0.01774006 -0.003572130   -0.01416793 -0.01416793
## 4: 15850.76 99.113 95.293 -0.01545781 -0.003982698   -0.01147511 -0.01147511
## 5: 16931.42 99.181 95.106 -0.01684658 -0.004393402   -0.01245318 -0.01245318
## 6: 17470.23 98.984 94.523 -0.01754700 -0.004598437   -0.01294856 -0.01294856
##    oxy.unit time.unit  volume     mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1493000
## 2:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1424719
## 3:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1477567
## 4:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1196734
## 5:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1298737
## 6:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.1350400
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -307.8350          NA  mgO2/hr/kg   -307.8350
## 2:   -293.7564          NA  mgO2/hr/kg   -293.7564
## 3:   -304.6531          NA  mgO2/hr/kg   -304.6531
## 4:   -246.7493          NA  mgO2/hr/kg   -246.7493
## 5:   -267.7808          NA  mgO2/hr/kg   -267.7808
## 6:   -278.4330          NA  mgO2/hr/kg   -278.4330
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 7 CARL338 CARL345 Arlington reef 105 0.000485 ch2 Asus 0.04573 2023-05-10 2024-06-18 good/good 36 28.5 290.4917 0.1408885 0.99

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 12 13 14 15 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.42
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     360.5740 -0.04937526 0.9988583      NA 103    148 5343.69
##   2:  NA    2     360.3381 -0.04933167 0.9988110      NA 102    147 5342.34
##   3:  NA    3     360.2532 -0.04931510 0.9987644      NA 104    149 5345.06
##   4:  NA    4     360.0780 -0.04928349 0.9987693      NA 101    146 5340.98
##   5:  NA    5     359.5529 -0.04918583 0.9987343      NA 100    145 5339.62
##  ---                                                                       
## 172:  NA  172     198.2939 -0.01896401 0.9870660      NA  30     75 5244.60
## 173:  NA  173     198.0609 -0.01891892 0.9872669      NA  28     73 5241.90
## 174:  NA  174     198.0411 -0.01891484 0.9872849      NA  27     72 5240.54
## 175:  NA  175     193.1653 -0.01856021 0.9371468      NA 175    220 5441.38
## 176:  NA  176     190.0074 -0.01798368 0.9401856      NA 176    221 5442.76
##      endtime    oxy endoxy        rate
##   1: 5403.69 96.731 93.794 -0.04937526
##   2: 5402.34 96.802 93.851 -0.04933167
##   3: 5405.06 96.673 93.770 -0.04931510
##   4: 5400.98 96.873 93.889 -0.04928349
##   5: 5399.62 96.949 93.927 -0.04918583
##  ---                                  
## 172: 5304.60 98.932 97.627 -0.01896401
## 173: 5301.90 98.950 97.716 -0.01891892
## 174: 5300.54 99.009 97.741 -0.01891484
## 175: 5501.38 92.414 91.123 -0.01856021
## 176: 5502.76 92.353 91.105 -0.01798368
## 
## Regressions : 176 | Results : 176 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 176 adjusted rate(s):
## Rate          : -0.04937526
## Adjustment    : 5.740002e-05
## Adjusted Rate : -0.04943266 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 3 rate(s) removed, 173 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 172 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1      360.574 -0.04937526 0.9988583      NA 103    148 5343.69
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5403.69 96.731 93.794 -0.04937526 5.740002e-05   -0.04943266 -0.04943266
##    oxy.unit time.unit  volume     mass area  S    t        P  rate.abs
## 1:     %Air       sec 0.04573 0.000485   NA 36 28.5 1.013253 -0.515531
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -1062.95          NA  mgO2/hr/kg    -1062.95
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 7 CARL338 CARL345 Arlington reef 105 0.000485 ch2 Asus 0.04573 2023-05-10 2024-06-18 good/good 36 28.5 290.4917 0.1408885 0.99 1062.95 0.515531 0.9988583 772.4588 0.3746425
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 129 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Replicate = 8 
mass = 0.0008244 
chamber = "ch1" 
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""

##--- time of trail ---## 
experiment_mmr_date_asus <- "10 May 2023 01 37PM/Oxygen"
experiment_mmr_date2_asus <- "10 May 2023 01 37PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 
## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 
## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre
## [1] -0.00242219

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post2 <- post_cycle2 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post3 <- post_cycle3 %>% calc_rate.bg() 
## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------
bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 
## [1] -0.003374601

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  4  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.49
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 
## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...
## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")
## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.
## adjust_rate: Rate adjustments applied using "linear" method.
apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.
apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)
## select_rate: Selecting rates with rsq values between 0.95 and 1...
## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----
## select_rate: Selecting lowest 6 *absolute* rate values...
## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  13    1     484.2683 -0.02865619 0.995      NA 4812   4992 13446.11
## 2:  17    1     550.1535 -0.02891909 0.994      NA 6403   6583 15606.06
## 3:  18    1     564.4140 -0.02881733 0.998      NA 6801   6981 16146.61
## 4:  19    1     513.9827 -0.02485711 0.996      NA 7198   7379 16685.90
## 5:  20    1     501.7660 -0.02338857 0.997      NA 7596   7776 17225.67
## 6:  21    1     549.9600 -0.02536274 0.993      NA 7994   8174 17766.16
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 13690.78 98.687 91.609 -0.02865619 -0.003407832   -0.02524836 -0.02524836
## 2: 15850.76 98.395 91.696 -0.02891909 -0.003659290   -0.02525980 -0.02525980
## 3: 16391.19 98.927 92.131 -0.02881733 -0.003722212   -0.02509512 -0.02509512
## 4: 16931.42 98.978 92.698 -0.02485711 -0.003785050   -0.02107206 -0.02107206
## 5: 17470.23 98.846 93.034 -0.02338857 -0.003847833   -0.01954074 -0.01954074
## 6: 18010.61 99.203 92.709 -0.02536274 -0.003910749   -0.02145200 -0.02145200
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2628534
## 2:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2629725
## 3:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2612580
## 4:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2193752
## 5:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2034330
## 6:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.2233305
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -318.8420          NA  mgO2/hr/kg   -318.8420
## 2:   -318.9865          NA  mgO2/hr/kg   -318.9865
## 3:   -316.9069          NA  mgO2/hr/kg   -316.9069
## 4:   -266.1028          NA  mgO2/hr/kg   -266.1028
## 5:   -246.7649          NA  mgO2/hr/kg   -246.7649
## 6:   -270.9007          NA  mgO2/hr/kg   -270.9007
## -----------------------------------------
## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest
96 8 CARL338 CARL345 Arlington reef 105 0.0008244 ch1 Asus 0.04565 2023-05-10 2024-06-18 good/good 36 28.5 298.3478 0.2459579 0.9952

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start
## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 
## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="
cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 
## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.
inspect(cycle1_data)
## inspect: Applying column default of 'time = 1'
## inspect: Applying column default of 'oxygen = 2'
## Warning: inspect: Time values are not evenly-spaced (numerically).
## inspect: Data issues detected. For more information use print().
## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  6  7  9 11 12 14 15 16 17 18 20 21 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.33 1.42
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     534.2656 -0.06209438 0.9971292      NA 177    222 7160.90
##   2:  NA    2     531.9167 -0.06176756 0.9972280      NA 176    221 7159.55
##   3:  NA    3     529.5064 -0.06143214 0.9973410      NA 175    220 7158.19
##   4:  NA    4     527.2268 -0.06111477 0.9975056      NA 174    219 7156.83
##   5:  NA    5     524.3476 -0.06071394 0.9976866      NA 173    218 7155.45
##  ---                                                                       
## 173:  NA  173     250.3571 -0.02181633 0.9403144      NA   5     50 6927.83
## 174:  NA  174     243.7309 -0.02086370 0.9396537      NA   4     49 6926.48
## 175:  NA  175     237.7158 -0.01999869 0.9398699      NA   3     48 6925.14
## 176:  NA  176     231.6691 -0.01912922 0.9351991      NA   2     47 6923.78
## 177:  NA  177     227.4008 -0.01851531 0.9318964      NA   1     46 6922.43
##      endtime    oxy endoxy        rate
##   1: 7220.90 89.617 85.796 -0.06209438
##   2: 7219.55 89.691 85.887 -0.06176756
##   3: 7218.19 89.780 85.976 -0.06143214
##   4: 7216.83 89.830 86.057 -0.06111477
##   5: 7215.45 89.894 86.161 -0.06071394
##  ---                                  
## 173: 6987.83 99.045 97.669 -0.02181633
## 174: 6986.48 99.073 97.741 -0.02086370
## 175: 6985.14 99.036 97.832 -0.01999869
## 176: 6983.78 99.101 97.936 -0.01912922
## 177: 6982.43 99.114 98.032 -0.01851531
## 
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj
## adjust_rate: Rate adjustments applied using "mean" method.
## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 177 adjusted rate(s):
## Rate          : -0.06209438
## Adjustment    : -0.00242219
## Adjusted Rate : -0.05967219 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)
## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.93,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)
## select_rate: Selecting rates with rsq values between 0.93 and 1...
## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----
## select_rate: Selecting highest 1 *absolute* rate values...
## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----
## 
## # plot.convert_rate # -------------------
## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow   time
## 1:  NA    1     534.2656 -0.06209438 0.9971292      NA 177    222 7160.9
##    endtime    oxy endoxy        rate  adjustment rate.adjusted  rate.input
## 1:  7220.9 89.617 85.796 -0.06209438 -0.00242219   -0.05967219 -0.05967219
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0008244   NA 36 28.5 1.013253 -0.6212299
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:    -753.554          NA  mgO2/hr/kg    -753.554
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 
Clutch Replicate Male Female Population Tank Mass Chamber System Volume Date_tested Date_analysed Swim Salinity Temperature Resting_kg Resting rsqrest Max_kg Max rsqmax AAS_kg AAS Notes True_resting
96 8 CARL338 CARL345 Arlington reef 105 0.0008244 ch1 Asus 0.04565 2023-05-10 2024-06-18 good/good 36 28.5 298.3478 0.2459579 0.9952 753.554 0.6212299 0.9971292 455.2062 0.375272
### Expor ting data
resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 
## Rows: 130 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 
write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)